BI Analyst Roadmap¶
- Roadmap: https://roadmap.sh/bi-analyst
1. Introduction¶
- 1.1 What is BI?
- 1.2 Why BI Matters?
- 1.3 BI Analyst vs Other Roles
- 1.4 Skills
- 1.5 Responsibilities
1.6 Business Fundamentals¶
- 1.6.1 Finance
- 1.6.2 Marketing
- 1.6.3 Operations
- 1.6.4 HR
1.7 Key Business Functions¶
- 1.7.1 Metrics and KPIs
- 1.7.2 Stakeholder Identification
- 1.7.3 Types of BI Operations
- 1.7.3.1 Operational BI
- 1.7.3.2 Tactical BI
- 1.7.3.3 Strategic BI
1.8 Types of Data Analysis¶
- 1.8.1 Descriptive Analysis
- 1.8.2 Diagnostic Analysis
- 1.8.3 Predictive Analysis
- 1.8.4 Prescriptive Analysis
1.9 Variables and Data Types¶
- 1.9.1 Categorical vs Numerical
- 1.9.2 Discrete vs Continuous
2. Statistics Basics¶
2.1 Descriptive Statistics¶
- 2.1.1 Central Tendency
- 2.1.1.1 Mean
- 2.1.1.2 Median
- 2.1.1.3 Mode
- 2.1.2 Dispersion
- 2.1.2.1 Range
- 2.1.2.2 Variance
- 2.1.2.3 STD
- 2.1.2.4 IQR
2.2 Correlation vs Causation¶
- 2.2.1 Correlation Analysis
- 2.2.2 Regression Analysis
- 2.2.2.1 Linear Regression
- 2.2.2.2 Beyond Linear Regression
2.3 Inferential Statistics¶
- 2.3.1 Distribution
- 2.3.1.1 Skewness
- 2.3.1.2 Kurtosis
2.4 Hypothesis Testing¶
- 2.4.1 Population & Sample
- 2.4.2 Statistical tests
- 2.4.3 p-value
- 2.4.4 Confidence Intervals
- 2.4.5 Types of Errors
3. BI Core Skills¶
3.1 What is Data?¶
- 3.1.1 Analog vs Digital Data
3.2 Types of Data¶
- 3.2.1 Structured
- 3.2.2 Semistructured
- 3.2.3 Unstructured
3.3 Data Sources¶
- 3.3.1 Databases
- 3.3.2 Web
- 3.3.3 Mobile Apps
- 3.3.4 Cloud
- 3.3.5 APIs
- 3.3.6 IoT
3.4 Data Formats¶
- 3.4.1 Excel
- 3.4.2 CSV
- 3.4.3 JSON
- 3.4.4 XML
- 3.4.5 Other formats
3.5 Popular Databases¶
- 3.5.1 MySQL
- 3.5.2 PostgreSQL
- 3.5.3 SQLite
- 3.5.4 Oracle
3.6 SQL Fundamentals¶
- 3.6.1 Basic Queries
- 3.6.2 Advanced Queries
- 3.6.3 Window Functions
- 3.6.4 Performance
- 3.6.5 Visit SQL Roadmap
3.7 Data Cleaning¶
- 3.7.1 Data Transformation Techniques
- 3.7.2 Standardisation
- 3.7.3 Missing Values
- 3.7.4 Duplicates
- 3.7.5 Outliers
- 3.7.6 Tools for Data Cleaning
- 3.7.6.1 Excel
- 3.7.6.2 SQL
- 3.7.6.3 Pandas
- 3.7.6.4 dplyr
3.8 Exploratory Data Analysis (EDA)¶
4. Visualizing Data¶
4.1 Visualization Fundamentals¶
- 4.1.1 Color theory
- 4.1.2 Accessibility
- 4.1.3 Design principles
- 4.1.4 Misleading charts
- 4.1.5 Mobile-responsiveness
4.2 Chart Categories¶
4.3 Visualization Best Practices¶
4.4 Popular Plots¶
- 4.4.1 Barplot
- 4.4.2 Lineplot
- 4.4.3 Histogram
- 4.4.4 Scatterplot
- 4.4.5 Heatmap
- 4.4.6 Map
5. BI Tools¶
5.1 Excel¶
5.2 BI Platforms¶
- 5.2.1 Power BI
- 5.2.2 Tableau
- 5.2.3 Qlik
- 5.2.4 Looker
6. Cloud Computing¶
- 6.1 Cloud Computing Basics
- 6.2 Cloud data warehouses
- 6.3 Providers: AWS, GCP, Azure
6.4 Programming Languages¶
- 6.4.1 Python
- 6.4.2 R
7. Business Applications¶
7.1 Finance¶
- 7.1.1 Sales Performance
- 7.1.2 Inventory Optimization
- 7.1.3 Marketing Campaigns
- 7.1.4 Supply Chain Analytics
- 7.1.5 Risk Analytics
- 7.1.6 Compliance Reporting
- 7.1.7 Financial Performance
- 7.1.8 Fraud Detection
- 7.1.9 CLV
7.2 Retail & E-commerce¶
7.3 Healthcare¶
- 7.3.1 Patient management
- 7.3.2 Hospital Efficiency
- 7.3.3 Compliance Reporting
- 7.3.4 Public Health
7.4 Manufacturing¶
- 7.4.1 Predictive Maintenance
- 7.4.2 Supply chain optimization
- 7.4.3 Production Efficiency
- 7.4.4 Quality Control
8. BI Techniques¶
8.1 Time Series Analysis¶
- 8.1.1 Seasonality
- 8.1.2 Trends
- 8.1.3 Forecasting
8.2 A/B Testing¶
- 8.2.1 Cohort Analysis
8.3 Basic Machine Learning¶
- 8.3.1 Supervised Learning
- 8.3.2 Unsupervised Learning
- 8.3.3 Reinforcement Learning
- 8.3.4 Algorithmic Bias
- 8.3.5 Mitigation Strategies
9. Professional Excellence¶
9.1 Communication & Storytelling¶
- 9.1.1 Storytelling Framework
- 9.1.2 Presentation Design
- 9.1.3 Dashboard Design
- 9.1.4 Writing Executive Summaries
9.2 Soft Skills¶
- 9.2.1 Business Acumen
- 9.2.2 Critical Thinking
- 9.2.3 Project Management
- 9.2.4 Change Management
- 9.2.5 Stakeholder Management
10. Data Governance & Ethics¶
10.1 Ethical Data Use¶
- 10.1.1 Bias Recognition
10.2 Data Quality¶
- 10.2.1 Relevance
- 10.2.2 Timeliness
- 10.2.3 Accessibility
- 10.2.4 Interpretability
- 10.2.5 Accuracy
- 10.2.6 Coherence
10.3 Data Lineage¶
10.4 Privacy¶
- 10.4.1 GDPR
- 10.4.2 CCPA
11. Data Architectures¶
- 11.1 Data Warehouse
- 11.2 Data Lake
- 11.3 Data Mart
- 11.4 Cloud BI Ecosystem
11.5 Data Modeling for BI¶
- 11.5.1 Normalization vs Denormalization
- 11.5.2 Fact vs Dimension Tables
- 11.5.3 Star vs Snowflake Schema
- 11.5.4 Calculated Fields & Measures
11.6 ETL Tools¶
- 11.6.1 ETL basics
- 11.6.2 Airflow
- 11.6.3 dbt
12. Career Development¶
12.1 Building Your Portfolio¶
- 12.1.1 End-to-end Analytics Project
- 12.1.2 Dashboard Design
- 12.1.3 Data Pipeline Design
12.2 Job Preparation¶
- 12.2.1 Resume optimization
- 12.2.2 Portfolio presentation
- 12.2.3 Interview preparation
- 12.2.4 Salary negotiation strategies
12.3 Professional Development¶
- 12.3.1 Certifications
- 12.3.2 Networking
- 12.3.3 BI Communities
- 12.3.4 BI Competitions
- 12.3.5 Open-Source Projects
- 12.3.6 Conferences & Webinars